Classification-Based Inference of Dynamical Models of Gene Regulatory Networks

We present FIGR (Fast Inference of Gene Regulation), a novel classification-based inference approach to determining gene circuit parameters. We demonstrate FIGR’s effectiveness on synthetic data generated from random gene circuits of up to 50 genes as well as experimental data from the gap gene system of Drosophila melanogaster, a benchmark for inferring dynamical GRN models. FIGR is faster than global non-linear optimization by a factor of 600 and its computational complexity scales much better with GRN size. On a practical level, FIGR can accurately infer the biologically realistic gap gene network in under a minute on desktop-class hardware instead of requiring hours of parallel computing. We anticipate that FIGR would enable the inference of much larger biologically realistic GRNs than was possible before.

Source: G3: Genes Genomes Genetics - December 2, 2019 Category: Genetics & Stem Cells Authors: Fehr, D. A., Handzlik, J. E., Manu, , Loh, Y. L. Tags: Investigations Source Type: research